This section provides background information related to the present disclosure which is not necessarily prior art.
Energy usage is a major concern for data centers. As data centers becomes larger and larger, often with thousands, or even tens of thousands, of components located in one or more rooms of a data center, the need to minimize energy consumption of CRAC (computer room air conditioner) units, while still providing adequate cooling, is becoming even more important.
One important consideration in managing the energy consumption of data center cooling equipment such as CRAG units is in monitoring and adjusting the supply air temperature (“SAT”) to optimize room conditions. Traditionally this has involved a user manually, periodically adjusting a SAT input by the user, in an effort to best meet physical temperature conditions in the room while attempting to minimize energy usage. As will be appreciated, this approach relies on the attention, knowledge and experience of a user in manually selecting the optimal SAT setpoint, as well as other parameters for CRAC units being used within the room. An automated system that is capable of “learning”, based on past system behavior, how to best select the SAT setpoint, would enable energy usage to be optimized. Importantly, such an automated, intelligent system would also eliminate reliance, or reduce reliance, on the experience and knowledge of a user in selecting an optimal SAT setpoint to best meet the physical room conditions at any given time.